In this study, we propose methods for the automatic detection of photospheric features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are applied to segment the bright points (BPs) and granules, respectively. Zernike moments of each region are computed. The Zernike moments of BPs, granules, and other features are distinctive enough to be separated using a support vector machine (SVM) classifier.The size distribution of BPs can be fitted with a power-law slope -1.5. The peak value of granule sizes is found to be about 0.5 arcsec 2 . The mean value of the filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small granules with sizes smaller than 2.5 arcsec 2 cover a wide range of brightness, while the brightness of large granules approaches unity. The mean value of BP brightness fluctuations is estimated to be 1.2, while for granules it is 0.22. Mean values of the horizontal velocities of an individual BP and an individual BP within the network were found to be 1.6 km s −1 and 0.9 km s −1 , respectively. We conclude that the effect of individual BPs in releasing energy to the photosphere and maybe the upper layers is stronger than what the individual BPs release into the network.

We investigate the characteristics of the solar flare complex network. The limited predictability, nonlinearity, and self-organized criticality of the flares allow us to study systems of flares in the field of the complex systems. Both the occurrence time and the location of flares detected from 2006 January 1 to 2016 July 21 are used to design the growing flares network. The solar surface is divided into cells with equal areas. The cells, which include flares, are considered nodes of the network. The related links are equivalent to sympathetic flaring. The extracted features demonstrate that the network of flares follows quantitative measures of complexity. The power-law nature of the connectivity distribution with a degree exponent greater than three reveals that flares form a scale-free and small-world network. A large value for the clustering coefficient, a small characteristic path length, and a slow change of the diameter are all characteristics of the flares network. We show that the degree correlation of the flares network has the characteristics of a disassortative network. About 11% of the large energetic flares (M and X types in GOES classification) that occurred in the network hubs cover 3% of the solar surface.

The solar corona is the origin of very dynamic events that are mostly produced in active regions (AR) and coronal holes (CH). The exact location of these large-scale features can be determined by applying image-processing approaches to extreme-ultraviolet (EUV) data.We here investigate the problem of segmentation of solar EUV images into ARs, CHs, and quiet-Sun (QS) images in a firm Bayesian way. On the basis of Bayes' rule, we need to obtain both prior and likelihood models. To find the prior model of an image, we used a Potts model in non-local mode. To construct the likelihood model, we combined a mixture of a Markov-Gauss model and non-local means. After estimating labels and hyperparameters with the Gibbs estimator, cellular learning automata were employed to determine the label of each pixel.We applied the proposed method to a Solar Dynamics Observatory/ Atmospheric Imaging Assembly (SDO/AIA) dataset recorded during 2011 and found that the mean value of the filling factor of ARs is 0.032 and 0.057 for CHs. The power-law exponents of the size distribution of ARs and CHs were obtained to be -1.597 and -1.508, respectively, with the maximum likelihood estimator method. When we compare the filling factors of our method with a manual selection approach and the SPoCA algorithm, they are highly compatible.

Aims: The statistics of the photospheric granulation pattern are investigated using continuum images observed by Solar Dynamic Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) taken at 6713 Å.Methods: The supergranular boundaries can be extracted by tracking photospheric velocity plasma flows. The local ball-tracking method is employed to apply on the HMI data gathered over the years 2011-2015 to estimate the boundaries of the cells. The edge sharpening techniques are exerted on the output of balltracking to precisely identify the cells borders. To study the fractal dimensionality (FD) of supergranulation, the box counting method is used.Results: We found that both the size and eccentricity follow the log-normal distributions with peak values about 330 Mm 2 and 0.85, respectively. The five-year mean value of the cells number appeared in half-hour sequences is obtained to be about 60 ± 6 within an area of 350 ′′ × 350 ′′ . The cells orientation distribution presents the power-law behavior.Conclusions: The orientation of supergranular cells (O) and their size (S ) follows a power-law function as |O| ∝ S 9.5 . We found that the non-roundish cells with smaller and larger sizes than 600 Mm 2 are aligned and perpendicular with the solar rotational velocity on the photosphere, respectively. The FD analysis shows that the supergranular cells form the self-similar patterns.

With the advent of new high-resolution instruments for detecting and studying radio galaxies with different morphologies, the need for the use of automatic classification methods is undeniable. Here, we focused on the morphological-based classification of radio galaxies known as Fanaroff–Riley (FR) type I and type II via supervised machine-learning approaches. Galaxy images with a resolution of 5″ at 1.4 GHz provided by the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey are employed. The radial Zernike polynomials are exploited to extract image moments. Then, the rotation, translation, and scale-invariant moments of images are used to form a training set (65% of the radio galaxy sample) and a test set (the remaining 35%). The classes of the test set are determined by two classifiers: a support vector machine and a twin support vector machine (TWSVM). In addition the genetic algorithm is employed to optimize the length of moment series and to find the optimum values of the parameters of the classifiers. The labels of outputs are compared to identify the best performance classifier. To do this the confidence level of classifications is estimated by four different metrics: precision, recall, F1 score, and accuracy. All tests show that implementing TWSVM with the radial basis function as a kernel achieves a confidence level of more than 95% in grouping galaxies.